Inductive, Deductive & Hybrid Coding: Choosing the Right Approach
Learn about inductive, deductive, and hybrid approaches to qualitative coding. Discover how to choose the right method based on your research aims and questions.
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Qualitative Coding Approaches Inductive, Deductive Abductive - Simple Explainer With Examples
Added on 08/28/2024
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Speaker 1: Qualitative coding is a topic that often leaves students feeling a little confused, but it doesn't have to be that way. In this video, we'll unpack and explore the three overarching approaches to qualitative coding, inductive, deductive, and hybrid, so that you can choose the best option for your project. Let's do it. Hey, my name's Emma, and today we're going to explore the sometimes intimidating world of qualitative coding. Now, this video is based on a lesson from our popular short course, Qualitative Research Bootcamp. If you're feeling a little overwhelmed by the qualitative realm, be sure to check that out. We've included a link with a special 50% discount offer in the description below. Alright, so let's start with the basics and ask the question, what exactly is qualitative coding? Well, simply put, qualitative coding is the process of categorizing and labeling textual data to lay the foundation for identifying themes, patterns, and ultimately, insights. In other words, it's the first step towards qualitative data analysis. In practical terms, coding involves meticulously reading through a dataset. For example, interview transcripts, field notes, or documents, and assigning codes to various excerpts from the text. These codes can be words, phrases, or short little summaries that capture the essence of each data segment. That probably sounds a little bit fluffy and conceptual, so let's look at a practical example. Imagine you have an interview transcript where a participant discusses their experience with a specific online learning platform. A segment of the transcript might read, I found online classes challenging because I struggled with time management and staying motivated. When it comes to coding this transcript, you might assign codes like time management challenges and motivation issues to this specific passage. In other words, you'd be labeling and categorizing snippets of text as you work your way through the transcript. Now, the exact pieces of text you decide to label and which specific codes you use will depend on the coding structure that you adopt, as well as your research aims and research questions. We explain different coding structures in a separate video, which we've linked to in the description, so be sure to check that out. For now though, the key takeaway is that coding is all about categorizing and labeling textual data. All right, so now that we've defined what we mean by qualitative coding, we can push on to explore the three overarching approaches to coding, that is inductive, deductive, and hybrid coding. Let's unpack each of these. First up, let's look at the inductive approach to qualitative coding. In simple terms, the inductive approach involves developing codes based on the data itself, as opposed to approaching the data set with a predetermined set of codes based on existing theory. So, in practical terms, this means that you, as the researcher, start the coding process with no preconceived codes. Instead, you'll read through each passage of text and allow the codes to emerge organically from the data based on the patterns that you see. In other words, the inductive approach is bottom-up and iterative. This makes it ideal for exploratory research, especially when there is limited existing theory and understanding of a specific phenomenon. Next up, let's travel to the other end of the coding spectrum to look at deductive coding. In contrast to inductive coding, the deductive approach uses an articulated theory or existing theoretical framework as a basis for a predefined set of codes. This set of codes is developed in advance and is typically contained within something called a code book. In practical terms, deductive coding means that you'll approach the data with a set of predefined codes and simply apply these codes to the data as you identify relevant passages or words. Importantly, with this approach, you don't develop any new codes while coding, even if you see patterns in the data that aren't represented by the existing code set. This approach probably sounds a little rigid, and it is, but this top-down approach is useful when your research aims are more confirmatory in nature. In other words, the deductive approach can work well when your research aims involve testing a theory rather than exploring phenomena. For example, if you were undertaking a dissertation where you're assessing the relevance of a specific motivation theory to a unique context, you might consider using the deductive approach. By the way, if you are currently working on a dissertation or thesis, be sure to check out our collection of free templates over on the Grad Coach blog. There, you can also find loads of other free resources, including detailed explainer videos, step-by-step tutorials, and much, much more. You can find the link to all of those in the description. All right, last but certainly not least, let's look at hybrid coding, which is sometimes also referred to as abductive coding. As the name suggests, hybrid coding combines the inductive and deductive approaches in an attempt to get the best of both worlds. With this approach, you might start with some predefined codes and then proceed to develop additional codes based on the patterns you observe along the way. Naturally, the hybrid approach to coding offers a good deal of flexibility. This makes it particularly effective for studies that incorporate both exploratory and confirmatory research aims. As you can see, the right coding approach, inductive, deductive, or hybrid, will largely depend on the nature of your research aims and research questions. If your aims are primarily exploratory, and there's not a large body of existing research regarding your topic, an inductive coding approach typically makes sense. Conversely, if your research aims involve confirming or even contradicting an existing theory, a deductive approach would likely be better suited. So, as with all methodological choices, your coding approach needs to be informed first and foremost by your research aims. All right, before we wrap up, it's worth quickly mentioning that there are various software options available to assist with the coding process. Popular options include Envivo, Delve, Atlas TI, and MaxQDA. Now, while these tools can certainly assist in terms of managing the coding process, it's important to understand that they're not essential, at least not for small datasets, which is commonly the case for student projects. For the vast majority of projects, you can code your dataset using a simple word processor, such as Microsoft Word or Google Docs. In fact, at Grad Coach, we code datasets for student projects every day using nothing more than Word and Excel. Taking a low-tech approach also helps you absorb and digest the data more deeply, as you naturally spend more time reading through it. So, while there are software options available, don't feel obligated to use them unless your university specifically requires you to do so. On the flip side, be sure to check if your university has any restrictions in terms of what software you can use, especially anything AI-powered. You don't want to run into a case of academic misconduct just because you used the wrong software. Now, if all of this sounds a bit intimidating, be sure to check out our private coaching service, where we can hold your hand through the coding process step-by-step. Alternatively, we also have a done-for-you coding service, where our qualitative experts meticulously code your data, laying a rock-solid foundation for your analysis. You can find the links to both of those services in the description. Alright, we've covered quite a bit of ground here, so let's do a quick recap. At the simplest level, qualitative coding is the process of categorizing and labeling textual data to help identify themes, patterns, and insights, which you'll explore in your qualitative analysis. There are three overarching approaches to coding—inductive, deductive, and hybrid. And, as always, your choice of approach needs to be informed by your research aims and research questions. If you enjoyed the video, please remember to hit the like and subscribe buttons so that we can keep you posted with the latest tips and tools. And be sure to check out this video next.

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